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Video reconstruction from a single motion-blurred image is a challengingproblem, which can enhance the capabilities of existing cameras. Recently,several works addressed this task using conventional imaging and deep learning.Yet, such purely-digital methods are inherently limited, due to directionambiguity and noise sensitivity. Some works proposed to address theselimitations using non-conventional image sensors, however, such sensors areextremely rare and expensive. To circumvent these limitations with simplermeans, we propose a hybrid optical-digital method for video reconstruction thatrequires only simple modifications to existing optical systems. We use alearned dynamic phase-coding in the lens aperture during the image acquisitionto encode the motion trajectories, which serve as prior information for thevideo reconstruction process. The proposed computational camera generates asharp frame burst of the scene at various frame rates from a single codedmotion-blurred image, using an image-to-video convolutional neural network. Wepresent advantages and improved performance compared to existing methods, usingboth simulations and a real-world camera prototype. We extend our opticalcoding also to video frame interpolation and present robust and improvedresults for noisy videos.

Video Reconstruction from a Single Motion Blurred Image using Learned Dynamic Phase Coding